Network Appliance NS0-901 Valid Vce | Exam NS0-901 Answers

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - Data management and data pipeline design - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks
AI Overview	15%	- AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics
AI Lifecycle	27%	- Data preparation and management for AI - Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance
Cloud and Hybrid Cloud AI Deployment	18%	- NetApp cloud data services for AI - Cloud-native AI solutions and integration - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures
Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency

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Exam NS0-901 Answers - Valid NS0-901 Test Vce

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
B. NetApp SnapLock to make the source data immutable before scanning.
C. A manual review process where technicians visually inspect each scan for PII.
D. A custom Python script that uses regular expressions to search file contents.
E. A custom "PII" category within BlueXP classification to identify specific medical record number formats.

Answer: A,E

NEW QUESTION # 17
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp E-Series
B. NetApp ASA (All-SAN Array)
C. NetApp StorageGRID
D. NetApp C-Series

Answer: B

NEW QUESTION # 18
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It sends an email notification to the administrator to manually reallocate the GPUs.
B. It automatically terminates all low-priority jobs to free up the entire cluster.
C. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
D. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.

Answer: C

NEW QUESTION # 19
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A. Manually create the PersistentVolumeClaim using 'kubectl'.
B. Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
C. Restart the BlueXP Connector to refresh its permissions.
D. Increase the storage quota for the 'jupyter-pvc-user01' PVC.

Answer: B

NEW QUESTION # 20
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

A. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
B. The data preparation pods do not have the correct security permissions.
C. The StorageGRID data lake is offline, preventing the creation of new volumes.
D. The Kubernetes scheduler is unable to find a node with enough resources for the pods.

Answer: A

NEW QUESTION # 21
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